Rivet Logic Blogs

Tag: AI

Our mission is to deliver riveting digital experiences for our healthcare clients. A new year always inspires a fresh look, and 2018 will bring a new (as well as continuing) set of challenges for healthcare executives. If you want to know what healthcare leaders are most concerned about, just ask them. Surveyors for Managed Healthcare Executive and the PwC Health Institute did precisely that.

The 2 Surveys Disclosed 5 Challenges

This post summarizes 5 Health IT challenges healthcare executives say are still top of their hit parade.

Only 12% of the survey responders reported that their organization is excelling in scooping up and harvesting all the data they generate and can harvest from other sources. While the percentage remains static from the 2016 survey, 46% of the respondents report they have come “a long way” in this area—up from 39% from last year.

Handicapping that progress is that, even though more healthcare data is generated, the information is scattered across multiple sources—patients, providers, and payers. There is no single source for healthcare data. Patients migrate between different health plans or providers, but the data does not follow them.

Most organizations do not have the technology to capitalize on big data. It is everywhere, but it is locked in silos with different formats and, again, from a variety of sources. To get at it, organizations need the big data technology infrastructure to get it, store it, and analyze it at a scale that is useable.

Our take on implications for healthcare clients: New ways to manage big data are growing at an explosive rate. It is all about aligning business goals with the technology. Rivet Logic’s big data solutions leverage the power of MongoDB to get a focused view of opportunities for cost reduction along with increases in productivity.

Challenge #2. Value-based Reimbursement Initiatives are lagging.

Value-based programs reward healthcare providers with incentive payments for the quality of care they provide to Medicare patients. Organizations continue to struggle in this area because the traditional fee-for-service system does not mesh well to a metrics- and outcome-driven value-based care approach. Also, delivering value-based care requires new infrastructure, workflow, and information sources, which are vastly different from those already in place.

How Rivet Logic can help you to migrate from fee-for-service to value-driven value-based care: Improving the patient experience is at the core of value-based care. Organizations need better collaborative processes and tools and the right mix of tools, which promote transparency and better internal communication.

That communication relies on patient profile management and turning the customer experience into a single data gathering session, which does not have to involve information overlap in data silos. For a detailed view of that process, download our data sheet to learn more about how address customer identity management.

Challenge #3. Patient experience must be a priority and not just a portal.

Just under half (49 percent) of provider executives reported that one of their top three priorities during the upcoming years will be revamping the patient experience. That effort will require healthcare organizations to “connect data points across and beyond the organization to understand how the patient’s experience fits” into the business.

Again, executives agree that it all centers around bring in multiple data sets. It requires “governing them, establishing ownership, and utilizing them to provide a real time, actionable information about the patient.”

Connectivity is the key. The patient experience is being transformed by technology. A connected health system requires better engagement of everyone—providers, their employees, and, most importantly, the patient. Digital solutions, like patient portals and mobile applications are supplanting visits to the office. Patients can self-monitor their conditions and transmit diagnostics over their smartphones. For more insight on this challenge and how Rivet Logic can help with that connectivity, download our data sheet to learn more about enabling better care with a connected health system.

Challenge #4. Securing the Internet of Things.

PwC predicts that there will be more cybersecurity breaches. So, hospitals and health systems need to be educated and prepared. PwC reported that 95 percent of the surveyed executives believed their organization is protected. However, only 36 percent had management access policies in place. Worse yet, only 34 percent could point to a cybersecurity audit process.

Managed Services is one solution. Rivet Logic provides a flexible and scalable array of automated processes, services, and on-demand infrastructure designed to reduce IT costs without sacrificing quality or security.

Challenge #5. Artificial intelligence will be a healthcare coworker.

Healthcare employees function best when automation takes over tiring, labor-intensive tasks. An average of 70 to 80 percent and Business executives reported that they plan to automate routine paperwork, scheduling, timesheet entry, and accounting with AI tools. In fact, a whopping 75 percent of healthcare executives “plan to invest in AI in the next three years.”

Again, managed services provide the pathway to keeping up with developments in IT in an environment of an expected continuing shortage of healthcare professionals.

Join us March 5-9, 2018 at HIMSS18

Rivet Logic will be exhibiting at HIMSS18 in Las Vegas in the Connected Health Experience pavilion. Discussions of approaches and solutions to the above-mentioned challenges–and much, much more–will be on the agenda, including:

Artificial intelligence has been floating around as a topic since the 1950s, so why it is suddenly coming to prominence in the language of marketers? Massive companies such as Microsoft, IBM, Apple, Facebook and Google’s parent Alphabet are making significant investments in the field in the hopes of garnering the additional market share promised by more intelligent user interactions. While messenger bots are still in their infancy, marketers everywhere are starry-eyed with the potential of offering instant self-service to customers in a way that feels very customized — and might even result in larger purchases and more consistent interactions. Are these automated systems a hit or a miss in the eyes of consumers? That all may depend on how well systems are integrated and the bots are programmed.

Types of Artificial Intelligence

Many of us are familiar with AI from hearing about chess matches between human masters and computers, as computers attempted to anticipate our next action. After years stuck in labs at MIT and Stanford, the field of artificial intelligence began to branch to natural language, with computers attempting to recreate the way humans select language to be used in a more conversational tone.

Machine learning is a particular type of AI that involves providing a computer with a vast quantity of data, and asking for predictions based on new data. As computers continue to aggregate information, this process becomes much more instinctive for machines. Another type of artificial intelligence involves programming artificial neural networks, an advanced concept that requires multiple layers of features in order to make better predictions. Machine learning that goes to this level is considered deep learning and it can require a high level of resources to execute it effectively.

Data-based Learning

The timeline for useful AI has accelerated in recent years, with Google and others making leaps in the field by feeding millions of images into a complex neural network, initially programming it to recognize cats within an image. From this breakthrough, Google has been a continued leader in AI by leveraging the functionality to bring enhancements to everything from Gmail to Street View and Google Translate. Google’s research scientists help fan the flame of AI interest by regularly publishing papers on their learnings, which in turn encourages others to continue their work in the field.

Amazon is another top organization utilizing AI in both their distribution center and on their website for enhanced recommendations. Consumers may not realize it, but Amazon’s Alexa uses the data from the millions of daily interactions to continue learning and improving both speech and intuitive customer recommendations.

The Rise of the Chatbots

The focus on AI as a marketing tactic is relatively new, and the explosion of chatbots in the last several years bears out the value that organizations are seeing as customers begin to record positive interactions. Most companies are still in the trial and error stage, but others are leveraging technology that is more mature. For example, the Cosmopolitan of Las Vegas hotel now “employs” an AI named Rose who interacts with hundreds of customers on a daily basis via text message — even tossing in kiss emojis when the situation demands. This sassy robotic lady helps extend the brand with customers while quickly solving everyday challenges such as concierge and housekeeping duties.

These chatbots appeal to individuals who are already using Facebook messenger or other programs such as WhatsApp to chat with friends, as they more seamlessly integrate to the tasks that customers need. Facebook now boasts over 100,000 bots that are actively chatting with customers and the continued innovation helps drive market interest and adoption. While interesting for basic needs where the conversation is unlikely to branch, AI is still in its infancy and many organizations are simply in beta testing or playing with chatbots instead of relying on them to perform critical business functions.

Integrating Chatbots with Your DXP or CMS Platform

Chatbots are not only exceptionally cool, but they can also integrate with your Digital Experience Platform (DXP) or website Content Management System (CMS) to deliver the ultimate in personalized experiences. This is especially true of organizations with an eCommerce component, as businesses are seeing double-digit sales and conversion rate improvements from chatbots versus social ads, for instance.

It’s important that chatbots are not treated as a siloed part of your marketing strategy, but instead are fully integrated into the overall experience. Chatbots are another channel for the dissemination of information, and should be fully integrated just as your email marketing and SMS messaging channels are. This is where a thorough knowledge of structured content comes into play. Instead of creating a separate grouping of content for your chatbot, a skilled partner will help you understand how to leverage the content you’re already creating for this fascinating new distribution channel.

Better Experiences?

There may still be some question about whether or not the chatbots offer a truly improved customer experience as opposed to working with a human customer service representative, for example. While chatbots are still relatively limited, they are able to quickly offer status updates, provide balances, let you know of special offers, detail which newsletters you wish to sign up for and complete purchases. As app downloads continue to decline and mobile-first websites grow in prominence, bots are an opportunity to reach customers where they already are: Facebook Messenger with 1 billion users per month, SMS texts and programs such as What’sApp, Slack and Kik. Chatbots do provide the one thing that it can be difficult to deliver in human-to-human interactions: personalization at scale.

As a field, data analytics is only growing. Not only has the industry of data science broadened substantially, but many companies are finding themselves devoting large amounts of resources towards understanding data analytics and trying to identify new trends. This reliance upon data is only going to grow through 2018, as companies are finding that the data that they collected may contain even more useful information than they previously believed.

2018 is going to find many companies making better use of the data that they already have, and fine-tuning their existing data collection and analysis methods.

Better Personalization Metrics

Industry leaders are hard at work creating incredibly detailed profiles of their customers. Companies don’t need to develop this information themselves. Google, for instance, has fine-tuned its customer profiles and made these customer profiles accessible to those using its analytics and advertising services. Social media platforms have been able to capture customer information similarly, from Facebook to LinkedIn.

The result of this is that advertising is likely to become hyper-personalized to each customer. Not only will companies know the demographics of each customer (age, gender, location), but also their buying habits, how much money they make, and which locations they frequent. Businesses will be able to increasingly target customers and anticipate their needs, ideally creating a situation in which advertising becomes more valuable to the customer.

Augmented Reality Systems

Augmented reality has been kicked around for the last decade, held back by issues regarding processing speed and (perhaps more importantly) battery life. Augmented reality feeds digital information about an individual’s location directly to them, often through a visible “heads up” display.

Not only is this going to change the way individuals interact with the world, but it’s also going to change the data collected. How often do users spend looking at a specific product? Which products or locations do they display further interest in? These will all create incredibly valuable data points that will again be used to create a realistic model of what customers want and need.

Streamlined Data Solutions

Companies have built up their data caches. Now they’re looking for streamlined, agile solutions that can help them make use of the data. In the past, companies were satisfied with collecting as much data as possible and then mining them for as many insights as they could find. Now, companies are more focused on fine-tuning their systems, generating and using the minimal amount of data they need for effective results.

This will create a rise in agile data science, whereby companies will be able to quickly create data sets, respond to and modify these data sets, and produce tangible results from their data sets. In this, the emphasis will be less on the data itself and more what the data can do for the company.

The Science of the Customer Journey

Buyer personas have led to further exploration of the customer journey, a science that attempts to identify the stages that customers go through when investigating and making a purchase. Customer journeys are an incredibly effective way to understand customers and their unique needs.

Data science is likely to be integrated into further understanding of the customer journey. What drives a customer to seek a product? How often does a customer generally research a product? What types of research are most effective and most compelling? What makes a customer more or less likely to find a company and engage in a purchase?

Customer journeys are designed to model customer behavior, so that companies are able to more accurately give customers the information and the prompts they need to continue their journey. In the coming year, this will evolve into a science of its own, and marketers will likely be collecting more customer behavior-related data than ever.

Machine Intelligence Continues to Advance

Alongside all of this, machine intelligence and machine learning will continue to advance. Many businesses have large volumes of data, but it is actually identifying patterns within that data that has become difficult. More advanced machine algorithms will be developed to clean usable, actionable insights from the data that is stored. Machine intelligence will increasingly be used for tasks such as scoring leads, identifying keywords, and targeting specific demographics.

More advanced, learning algorithms are being developed that can, within their parameters, work to improve their own functions. With the right data sets and the right code, marketing algorithms will be able to fine-tune themselves and optimize their own performance. This will be especially useful in A/B testing or split testing, as algorithms will be able to test out different marketing functions and determine the optimal configuration on their own.

Small amounts of this are already cropping up in apps and social media platforms, such as the ability of an algorithm to determine what is most likely to get a profile clicked on, or which photos and posts are most engaging. This can be used in a marketing sense to determine not only which products are most attractive to customers, but which photos they prefer to see, and what descriptions they’re most interested in.

For the most part, 2018 is going to see a maturation of data analytics and data science, as companies invest more money into both collecting and understanding their data. But technology itself is going to play a significant role as well, as the technology behind machine learning and AI is becoming more sophisticated and complex. Either way, companies are going to have to invest more in their data if they want to understand their customers and continue to market directly to them.

Is artificial intelligence the same as machine learning? Machine learning is really a subset of artificial intelligence, and a more precise way to view it is that it is state-of-the-art AI. Machine learning is a “current application of AI” and is centered around the notion that “we should…give machines access to data and let them learn for themselves.” There is no limit to that data (or Big Data). The challenge is harnessing it for useful purposes.

In his Forbes Magazine piece, contributor Bernard Marr, describes AI as the “broader concept of machines being able to carry out tasks in a way we would consider ‘smart.’” So, AI is any technique that allows computers to imitate human intelligence through logic, “if-then” rules, decisions trees and its crucial component, machine learning. Machine learning, as an application of AI, employs abstruse (i.e., difficult to understand) statistical techniques, which improve machine performance through exposure to Big Data.

AI has broad applications…

Companies around the world use AI in information technology, marketing, finance and accounting, and customer service. According to a Harvard Business Review article, IT garners the lion’s share of popularity in AI activities, ranging in applications that detect and deflect security intrusions, to automating production management work. Beyond security and industry, AI has broad applications in improving customer experiences with automatic ticketing, voice- and face-activated chat bots, and much more.

Machine learning is data analysis on steroids…

AI’s subset, machine learning, automates its own model building. Programmers design and use algorithms that are iterative, in that the models learn by repeated exposure to data. As the models encounter new data, they adapt and learn from previous computations. The repeatable decisions and results are based on experience, and the learning grows exponentially.

The return of machine learning

Having experienced somewhat of a slump in popularity, AI and machine learning have, according to one software industry commentator, Jnan Dash, seen “a sudden rise” in their deployment. Dash points to an acceleration in AI/machine learning technology and a market value jump “from $8B this year to $47B by 2020.”

Machine learning, according to one Baidu scientist will be the “new electricity,” which will transform technology. In other words, AI and machine learning will be to our future economy what electricity was to 20th century industry.

The big players are pushing AI and machine learning. Apple, Google, IBM, Microsoft and social media giants Facebook and Twitter are accelerating promoting machine learning. One Google spokesman, for example, recognizes machine learning as “a core transformative way in which we are rethinking everything we are doing.”

How Machine learning has transformed General Electric…

A striking example of how AI and machine learning are transforming one of the oldest American industries, General Electric, is highlighted in this Forbes piece. Fueled by the power of Big Data, GE has leveraged AI and machine learning in a remarkable—and ongoing—migration from an industrial, consumer products, and financial services firm “to a ‘digital industrial’ company” focusing on the “Industrial Internet.” As a result, GE realized $7 billion in software sales in 2016.

GE cashed in on data analytics and AI “to make sense of the massive volumes of Big Data” captured by its own industrial devices. Their insights on how the “Internet of Things” and machine connectivity were only the first steps in digital transformation led them to the realization that “making machines smarter required embedding AI into the machines and devices.”

After acquiring the necessary start-up expertise, GE figured out the best ways to collect all that data, analyze it, and generate the insights to make equipment run more efficiently. That, in turn, optimized every operation from supply chain to consumers.

Your business may be nowhere near the size of General Electric. You do, however, have a level playing field when it comes to leveraging Big Data and machine learning products to a winning strategy. What we do is help you plan that strategy by:

Aligning your business goals with technology—What are the sources of your own data and how can they harness the power of NoSQL databases, for example?

Designing your user experience—What do you need? A custom user interface, or a mobile app with intuitively simple user interfaces?

We can do that and much more. Contact us and we’ll help make your business future-ready to collect, harvest, and leverage all the great things you are doing now.